Granular Multilabel Batch Active Learning With Pairwise Label Correlation

Yuanjian, Zhang; Zhao, Tianna; Miao, Duoqian; Pedrycz, Witold

doi:10.1109/tsmc.2021.3062714

Cited by 20 publications

(3 citation statements)

References 46 publications

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“…With the rapid development in recent years, granular computing has been successfully applied in many fields. Zhang [27] et al combined granular computing with multi-label active learning to improve the accuracy of the algorithm. Fu [28] et al proposed a granular computing framework for hierarchical community detection.…”

Section: Introductionmentioning

confidence: 99%

BGRF: A broad granular random forest algorithm

Chen

Yan

et al. 2023

IFS

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The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm.

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Section: Introductionmentioning

confidence: 99%

BGRF: A broad granular random forest algorithm

Chen

Yan

et al. 2023

IFS

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“…Also, it will take more time to update the model caused by the addition sample in the next iteration. Batch AL [23][24][25] is fit for solving the issues of high computing consumption, in which multiple valuable samples are selected each time. The efficiency of AL can be improved with a smaller number of iterations.…”

Section: Introductionmentioning

confidence: 99%

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Jiang

Zhu

et al. 2023

Can J Chem Eng

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In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.

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“…Such routines may continue until the uncertainty is negligible and the classification performance is improved. Existing three-way-based multi-label classifications [28][29][30][31][32] deal with label uncertainty either from ensemble features or ensemble algorithms, whereas the ensemble on logical and numerical labels remains untouched.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

2022

Self Cite

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Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.

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Granular Multilabel Batch Active Learning With Pairwise Label Correlation

Cited by 20 publications

References 46 publications

BGRF: A broad granular random forest algorithm

BGRF: A broad granular random forest algorithm

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

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